PDF(Pubmed)
Abstract：
The rapid advancement of sequencing technologies has enabled the generation of vast datasets, allowing for the in-depth analysis of sequencing data. This analysis has facilitated the validation of novel pathogenesis hypotheses for understanding and treating diseases through ex vivo and in vivo experiments. Androgenetic alopecia (AGA), a common hair loss disorder, has been a key focus of investigators attempting to uncover its underlying mechanisms. Abnormal changes in mRNA, proteins, and metabolites have been identified in individuals with AGA, and future developments in sequencing technologies may reveal new biomarkers for AGA. By integrating multiple omics analysis datasets such as genomics, transcriptomics, proteomics, and metabolomics-along with clinical phenotype data-we can achieve a comprehensive understanding of the molecular underpinnings of AGA. This review summarizes the data-mining studies conducted on various omics analysis datasets as related to AGA that have been adopted to interpret the biological data obtained from different omics layers. We herein discuss the challenges of integrative omics analyses, and suggest that collaborative multi-omics studies can enhance the understanding of the complete pathomechanism(s) of AGA by focusing on the interaction networks comprising DNA, RNA, proteins, and metabolites.
摘要：
测序技术的快速发展使得大量数据集能够产生，允许测序数据的深入分析。该分析促进了通过离体和体内实验来理解和治疗疾病的新发病机理假设的验证。雄激素性脱发（AGA），一种常见的脱发障碍，一直是调查人员试图揭示其潜在机制的关键焦点。mRNA异常变化，蛋白质，和代谢物已经在患有AGA的个体中被鉴定出来，测序技术的未来发展可能揭示AGA的新生物标志物。通过整合基因组学等多个组学分析数据集，转录组学，蛋白质组学，和代谢组学-以及临床表型数据-我们可以全面了解AGA的分子基础。这篇综述总结了对与AGA相关的各种组学分析数据集进行的数据挖掘研究，这些数据集已被用来解释从不同组学层获得的生物学数据。我们在这里讨论整合组学分析的挑战，并建议协作的多组学研究可以通过关注包含DNA的相互作用网络来增强对AGA完整病理机制的理解，RNA,蛋白质，和代谢物。
